Computer vision technologies are useful for identifying objects of interest depicted in geospatial imagery such as satellite, street-level, and community-sourced images of real-world geospatial locations. However, state-of-the-art computer vision technologies are not completely accurate in identifying objects of interest, which introduces a level of error and uncertainty that is difficult to correct. For example, state-of-the-art computer vision technologies mis-identify some objects as objects of interest (i.e., false positive identifications), fail to identify some actual objects of interest (i.e., false negative identifications), mis-identify boundaries of detected objects of interest, and/or mis-identify attributes of detected objects of interest.